The impact of condom use on the HIV epidemic

Background: Condom promotion and supply was one the earliest interventions to be mobilized to address the HIV pandemic. Condoms are inexpensive and provide protection against transmission of HIV and other sexually transmitted diseases (STIs) as well as against unintended pregnancy. As many as 16 billion condoms may be used annually in all low- and middle-income countries (LMIC). In recent years the focus of HIV programs as been on testing and treatment and new technologies such as PrEP. Rates of condom use have stopped increasing short of UNAIDS targets and funding from donors is declining. Methods: We applied a mathematical HIV transmission model to 77 high HIV burden countries to estimate the number of HIV infections that would have occurred from 1990 to 2019 if condom use had remained at 1990 levels. Results: The results suggest that current levels of HIV would be five times higher without condom use and that the scale-up in condoms use averted about 117 million HIV infections. Conclusions: HIV programs should ensure that affordable condoms are consistently available and that the benefits of condom use are widely understood.


Introduction
The distribution and promotion of condoms has been a part of efforts to prevent HIV transmission since the beginning of the HIV response. Early programs often focused on ABC (Abstinence, Be faithful, use Condoms). Condoms provide triple protection, against the transmission of HIV and other sexually transmitted infections as well protection against unintended pregnancy 1 . Condom social marketing programs were the first HIV programs to reach national scale in many countries. The number of condoms distributed through social marketing programs increased from about 590 million annually in 1991 to 2.5 billion by 2012 before declining to about 1.7 billion in 2019 2 . Across 55 countries with a recent national household survey as part of the Demographic and Health Surveys (DHS) or AIDS Indicator Surveys (AIS) about 60 percent of men reported using a condom the last time they had sex with a non-marital, non-cohabiting partner and 65 percent report using a condom the last time they visited a sex worker (Table 1).
In all low-and middle-income countries about 16 billion condoms are used annually with about 7.5 billion used primarily for HIV prevention 1 . Since these figures are based on self-reports of condom use, they may over-state actual use. However, it is clear that large numbers of condoms have been procured and/ or distributed with the intention of helping users prevent HIV transmission.
Studies have shown condoms to be highly effective against HIV 3 , other sexually transmitted infections 4 and unintended pregnancy 5 . Consistent use is required to maximize an individual's protection. However, even inconsistent use will provide some benefit that can be large at a population-level 6 .
Across all DHS surveys about three-fifths of people report relying on the public sector for their condom supply. Social marketing programs provide nearly 2 billion condoms each year (https://www.dktinternational.org/contraceptive-social-marketing-statistics/), about Thus, international donor and national government funding for condom purchase, distribution and promotion plays a large role in supporting the widespread use of condoms.
The purpose of this paper is to investigate the global impact of condoms on the HIV epidemic through both retrospective and prospective analyses.

Methods
We used a publicly available mathematical simulation model, the Goals model 7 , to examine the impact of past and future condom use on the AIDS epidemic in 77 high burden countries. We used version 6.06 of the Goals model, which is available for free download at https://www.avenirhealth.org/software-spectrum.php. The source code for the calculations is available as Extended data 8 . This is the same model that was used to estimate epidemiological impact for the new UNAIDS Global HIV Strategy 9 .
Goals is a simulation model that calculates HIV transmission among different population risk groups (monogamous heterosexual couples, those with multiple heterosexual partners, female sex workers and clients, men who have sex with men (MSM), and people who inject drugs (PWID)) on the basis of their behaviors (number of partners, contacts per partner, condom use, age at first sex, needle sharing) and characteristics that influence transmission (presence of other sexually transmitted infections, stage of infection, male circumcision, and use of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP)). The model uses data on behaviors drawn from national surveys, such as DHS, and program data on the coverage of ART and programs to prevent mother-to-child transmission, PMTCT, from UNAIDS' HIV database. The model is fit to official estimates of HIV prevalence trends for each county, also available from UNAIDS.
HIV transmission is calculated as a function of epidemiological factors and the behavioral factors listed above. For uninfected people in each risk group, the probability of becoming infected in a year is given by the following equation: P s,r,t = {1-[Prev s',r,t × (1-r s × S s,r,t × STI s,r,t × MC t × C r,t × PrEP s,r,t × ART s,r,t ) a + (1-Prev s',r,t )] n } Where: P s,r,t = Annual probability of becoming infected for a person of sex s in risk group r at time t

Amendments from Version 1
This version has updates that respond to reviewers comments. It adds detail on data sources, more detail on the equations and expands the discussion.
Any further responses from the reviewers can be found at the end of the article  We applied the Goals model to 77 countries that together account for 94% of new infections globally in 2019 (https://aidsinfo.unaids.org/) and then scaled-up the result to correspond to the global epidemic. The full list of countries included is in Underlying data 8 . The model is implemented for each individual country by using country-specific data for demographic indicators (base year population, fertility, mortality, and migration) (https://population.un.org/wpp/), behavioral indicators (number and type of partners, condom use) from national household surveys (https://www.statcompiler.com/en/), and HIV program data (number of people on ART and number of women receiving prophylaxis to prevent mother-to-child transmission (PMTCT) and number of male circumcisions) (https://aidsinfo.unaids.org/). The model is fit to data on prevalence from surveys, surveillance, and routine testing by varying the epidemiological parameters within published ranges. The ranges used for the epidemiological parameters and the fitted values by country are provided in the underlying data.Historical trends in condom use by population group were estimated from self-reported condom use in DHS. Reported condom use in commercial sex was used for sex worker contact, reported use among those engaging in higher-risk sex was used for those with multiple partners and reported condom use for contraception was used for those with one partner. Information on the size of key populations is from the UNAIDS Key Population Atlas (https://kpatlas.unaids.org/).

REVISED
Once the model was fit to each country's actual epidemic we conducted three analyses: (1) Table 2.
We tested the sensitivity of the model results to the assumed effective of condoms in averting HIV infection by also running simulations with the effectiveness of condoms set to the low end of the 95% confidence interval (0.50) and with the high end (0.94).

Results
According to UNAIDS estimates, the annual number of new HIV infections worldwide increased to a peak of about 2.8 million   Figure 2 also shows that the rapid scale-up of condom use could produce about one-third the impact as the full UNAIDS strategy, which scales up all the intervention mentioned above to UNAIDS targets.

Discussion
Condom use has increased dramatically since the beginning of the HIV epidemic. Today, approximately 16 billion condoms are used annually to prevent infections and unintended pregnancies. Condom use has impacted the HIV epidemic and avoided a much worse HIV epidemic than has actually evolved. Condoms can play a key role in future efforts, such as the Fast-Track initiative to end AIDS as a public health threat by 2030 28 .
The number of HIV new infections under the retrospective counterfactual scenario of no increase in condom use after 1990, which reaches 11million by 2019, is quite high compared to the actual level of about 1.7 million. But this just illustrates the benefits of early intervention. Early increases in condom use among key populations, in particular sex workers and their clients, as well as with non-regular partners has slowed early transmission and helped to avert a much larger epidemic in the general population.
There are several limitations to this analysis. We rely on self-reports of condom use in national surveys that may over-state actual use. The effectiveness of condoms depends on correct and consistent use but measures of these factors are not well developed. Our modeling estimates the impact of condom use in aggregate population groups but does not model individual behavior. Using these data our models can replicate historical epidemic trends in the countries modeled but that does not ensure that they are correct. Findings of this analysis are, however, broadly consistent with other mathematical modelling analyses of the impact of condom use 29,30 . Estimates of the size of key populations in each country are based on small sample surveys which may not be representative of the entire country.
Estimates of the number of acts per partner are based on small studies or potentially unreliable self-reports. To some extent, these limitations are addressed by fitting the model to historical data on prevalence. While the fitting does not guarantee that all the inputs are correct, it does ensure that the set of inputs is sufficient to replicate the historical epidemic. In spite of above-mentioned limitations, the case for the importance of condoms as an ongoing component of HIV programming is compelling.
Previous modeling studies have shown the impact of historical condom scale-up in specific populations in specific-countries including sex workers in Benin 31 and MSM in Beijing, China 32 . Other studies have modeled the potential impact of programs to scale-up condom use, including adolescents in the United States 33 and hypothetical but representative settings 34 . All found significant impacts of condom use, but none examined the global impact. Condoms are a good investment. The total cost to prevent one new HIV infection with condoms is small compared to life-time costs of treatment meaning that condom investments now will save future expenditures on treatment. Since many people rely on free or subsidized condoms, it is crucial to ensure adequate funding for condom programs, including demand creation activities and frequent behavioral data collection.
While condoms are not a magic bullet that alone can control the HIV epidemic, they remain a critical part of the prevention response. Scale-up of condoms use is a necessary component to reach the UNAIDS global targets 9 and any reduction in support for condoms would seriously affect the changes of achieving those targets. Unfortunately, support for condom social marketing programs has been decreasing in recent years 35 . International and domestic financing should continue to support general population condom programs even as new technologies are introduced that are targeted to the highest risk populations. Condom programs remain among the most cost-effective interventions in the response and provide other health benefits including prevention of other sexually transmitted infections and protection against unwanted pregnancies 1 . Past experience has shown that we do know how to promote and distribute condoms and that many people will use them if they re available. Recent declines in condom investments especially around demand creation implies that the younger generation have not been exposed to relevant condom promotion and condom use skills, a worrisome trend given the relative size of young populations in low-and middle-income countries.
This project contains the following underlying data: Model: This manuscript does not describe the model in detail, unlike a modelling paper published by the authors (e.g., the PloS Medicine paper 1 ). The model for the incidence in the present study seems to differ substantially from the one in the PloS Medicine paper (Function (2) in S2 Text) (of course, the purpose of the modelling differed, too). Note that readers in this journal are not necessarily familiar with modelling studies. For example, readers may want to know different roles of Prev_s',r,t × (1-r_s × S_s,r,t × STI_s,r,t × MC_t × C_r,t × PrEP_s,r,t × ART_s,r,t)^a and (1 -Prev_s',r,t), reasons of using exponential functions with regard to the number of acts per partner and the number of partners. Although there is no citation for the model, the authors may want to add references if the model in the current study was built based on previous works.

Response:
We do provide a citation for the full model details when we say 'Complete model details are available elsewhere [24].' That reference provides equations and data sources. This is the same model used in the PLoS Medicine paper. We have added a citation to the PloS Medicine paper to make that clear. We have also added some clarification to the use of the number of acts per partner and the number of partners as exponents in the equation.
Values to be input in the model: The authors may want to describe how values of several key variables were obtained, such as the coverage of condom use among each of risk populations in 1990 and onward in each country, the estimated number ○ of these key populations in the past, present, and future years. The authors may want to describe assumptions in the estimated values, if any, in the Methods section and in the limitations in the Discussion section. In addition, values that were input in the model may need to be attached so that readers can verify the validity of the modelling. Response: We have added a description of the sources of information on historical condom use and the estimation of key population sizes. Table 1 shows the reported condom use rates by population group and country.
Sensitivity analysis: For future estimations, the authors may need to consider sensitivity analysis for different parameters and the past and future values of key variables. For example, it may not be realistic to have fixed values for the number of acts per partner and the number of partners among different key populations in the past or future years. In some countries, it may be difficult to obtain reliable sources for the current statistics for these numbers.
general population surveys, but this limitation has been duly acknowledged by the authors and reflected in the interpretation of results with a caveat that the analysis does not measure consistent condom use, which is a behavioral factor. of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.
from public sources and social marketing accounts for nearly 2 billion condoms each year.
Given its estimation of some 117 million new HIV infections averted since 1990 due to scale up of condom use, the paper should include a strong programmatic recommendation for effective integration of condom programming with other HIV prevention interventions, including sexual and reproductive health and rights.

Response:
We feel that we do make a strong recommendation for continued support for condom programming in the last paragraph of the discussion.
Competing Interests: No competing interests were disclosed.